Real-time location of acupuncture points based on anatomical landmarks and pose estimation models.

ABSTRACT:

Introduction

Precise identification of acupuncture points (acupoints) is essential for effective treatment, but manual location by untrained individuals can often lack accuracy and consistency. This study proposes two approaches that use artificial intelligence (AI) specifically computer vision to automatically and accurately identify acupoints on the face and hand in real-time, enhancing both precision and accessibility in acupuncture practices.

Methods

The first approach applies a real-time landmark detection system to locate 38 specific acupoints on the face and hand by translating anatomical landmarks from image data into acupoint coordinates. The second approach uses a convolutional neural network (CNN) specifically optimized for pose estimation to detect five key acupo

SUBMITTER: Malekroodi HS 

PROVIDER: S-EPMC11609928 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature